Proceedings of the Annual Meeting of the Cognitive Science Society

Incremental clustering is a type of categorization in which learning is unsupervised and changes to category structure occur gradually. While there has been little psychological research on this subject, several computational models for incremental clustering have been constructed. Although these models provide a good fit to data provided by some psychological studies, they overlook the importance of selective attention in incremental clustering. This paper compares the performance of two models, Anderson's (1990) rational model of categorization, and Fisher's (1987) C O B W E B , to that of human subjects in a task which stresses the importance of selective attention. In the study, subjects were shown a series of pictorial stimuli in one of two orders. The results showed that subjects focused their attention on the first extreme feature they saw, and later used this feature to classify ambiguous stimuli. Both models fail to predict human performance. These results indicate the need for a selective attention mechanism in incremental clustering as well as provide one constraint on h o w such a mechanism might work.